State-of-the-Art Nature-inspired Metaheuristic Algorithms for Optimization Problems

Nature-inspired metaheuristic algorithms are proved approaches for solving real-world complex optimization problems. Numerous works have been conducted on the development of metaheuristic optimization algorithms since the introduction of evolutionary algorithms. Almost all of these algorithms are inspired by biological phenomenon and are imitating the best characteristic in nature, which makes them powerful. As an example, genetic algorithm features crossover, mutation and selection operators simulating the biological evolution process. Particle Swarm Optimization (PSO), Cuckoo Search (CS) algorithm, Firefly Algorithm (FA), Bat Algorithm (BA), Harmony Search (HS), Ant Colony Optimization (ACO) etc. are of some well-known nature-inspired metaheuristic algorithms in the community. Laying Chicken Algorithm (LCA), Lion Optimization Algorithms (LOA) and Elephant Herding Optimization (EHO) are some recent developed algorithms. The flexibility and adaptability make these nature-inspired met heuristics algorithms popular among the research community. In this Editorial document, we present a state-of-the-Art of nature-inspired meta-heuristics algorithms for optimization problems.